On The Development of a Dynamic Contrast-Enhanced Near-Infrared Technique to Measure Cerebral Blood Flow in the Neurocritical Care Unit
Bibliographic record
Abstract
A dynamic contrast-enhanced (DCE) near-infrared (NIR) method to measure cerebral blood flow (CBF) in the neurocritical care unit (NCU) is described. A primary concern in managing patients with acquired brain injury (ABI) is onset of delayed ischemic injury (DII) caused by complications during the days to weeks following the initial insult, resulting in reduced CBF and impaired oxygen delivery.\nThe development of a safe, portable, and quantitative DCE-NIR method for measuring CBF in NCU patients is addressed by focusing on four main areas: designing a clinically compatible instrument, developing an appropriate analytical framework, creating a relevant ABI animal model, and validating the method against CT perfusion. In Chapter 2, depth-resolved continuous-wave NIR recovered values of CBF in a juvenile pig show strong correlation with CT perfusion CBF during mild ischemia and hyperemia (r=0.84, p<0.001). In particular, subject-specific light propagation modeling reduces the variability caused by extracerebral layer contamination. In Chapter 3, time-resolved (TR) NIR improves the signal sensitivity to brain tissue, and a relative CBF index is be both sensitive and specific to flow changes in the brain. In particular, when compared with the change in CBF measured with CT perfusion during hypocapnia, the deconvolution-based index has an error of 0.8%, compared to 21.8% with the time-to-peak method. To enable measurement of absolute CBF, a method for characterizing the AIF is described in Chapter 4, and the theoretical basis for an advanced analytical framework—the kinetic deconvolution optical reconstruction (KDOR)—is provided in Chapter 5. Finally, a multichannel TR-NIR system is combined with KDOR to quantify CBF in an adult pig model of ischemia (Chapter 6). In this final study, measurements of CBF obtained with the DCE-NIR technique show strong agreement with CT perfusion measurements of CBF in mild and moderate ischemia (r=0.86, p<0.001).\nThe principle conclusion of this thesis is that the DCE-NIR method, combining multidistance TR instrumentation with the KDOR analytical framework, can recover CBF values that are in strong agreement with CT perfusion values of CBF. Ultimately, bedside CBF measurements could improve clinical management of ABI by detecting delayed ischemia before permanent brain damage occurs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".